Three-year incidence of elevated albuminuria and associated factors in a population-based cohort: The Taichung Community Health Study

Three-year incidence of elevated albuminuria and associated factors in population- based cohort: Taichung Community Health Study

Li-Na Liao, MSc

, Chiu-Shong Liu, MD, MPH

, Chia-Ing Li, PhD

, Wen-Yuan Lin, MD, PhD

, Chih-Hsueh Lin, MD

, Tsai-Chung Li, PhD*

, and Cheng-Chieh Lin, MD, PhD*

1

Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan

2

Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan

3

School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan

4

Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

5

Ph.D. Program for Aging, College of Medicine, China Medical University, Taichung, Taiwan

6

Graduate Institute of Biostatistics, College of Management, China Medical University, Taichung, Taiwan

7

Department of Healthcare Administration, College of Health Science, Asia University, Taichung, Taiwan

#

Equal contribution as the first author

*Corresponding authors: Cheng-Chieh Lin and Tsai-Chung Li; correspondence to Tsai-Chung

Li, China Medical University, No. 91 Hsueh-Shih Road, Taichung, 40402, Taiwan, Tel: 886-

4-22053366 ext. 6605, Fax: 886-4-22078539, e-mail: [email protected].

Three-year incidence of elevated albuminuria and associated factors in population-based cohort: Taichung Community Health Study

Abstract

Background: Early-stage elevated albuminuria can be effectively detected by a spot urine albumin-to-creatinine ratio (UACR). Elevated albuminuria is a key predictor of diabetic nephropathy, progression to chronic kidney disease (CKD) or end-stage renal disease (ESRD), plus risk of cardiovascular disease (CVD) and mortality. Understanding these detectors may prevent future renal and cardiovascular disease. This study estimates three-year incidence in a representative sample of Taiwanese metropolitan adults to explore predictors.

Methods: Baseline survey, Taichung Community Health Study (TCHS), in a representative sample of 2,359 Chinese adults aged 40 years and over living in a metropolitan city during 2004- 2005. In 2007-2009, a total of 1,648 (71.3%) individuals participated in follow-up. This study includes only individuals with normal albumin excretion at baseline examination. Three-year incidence and baseline factors linked with elevated albuminuria were evaluated.

Results: About 87.0% (n=1,434) of subjects exhibited normal albumin excretion at baseline.

Three-year age- and gender-weighted incidence was 4.5% (95% CI: 3.4-5.6%). Multivariate logistic regression showed subjects with elevated waist-to-hip ratio (WHR) (OR: 2.2, 95% CI:

1.2-3.9), abnormal creatinine (OR: 3.7, 95% CI: 1.1-12.6), hyperuricemia (OR: 1.8, 95% CI: 1.0- 3.3) and elevated baseline UACR (OR: 4.0, 95% CI: 1.1-14.3 for UACR of 3.20-6.39 mg/g; OR:

16.7, 95% CI: 5.0-55.5 for UACR of 6.40-29.99 mg/g) were more likely to have elevated albuminuria.

Conclusions: This is the first population-based longitudinal study to rate incidence of elevated albuminuria and identify associated factors in a random sample of Chinese population. Central obesity, renal function, hyperuricemia, and baseline UACR are independent risk factors.

Keywords: albuminuria; incidence; population-based

Introduction

Prevalence and incidence of end-stage renal disease (ESRD) and chronic kidney disease (CKD) is rapidly rising in Taiwan. The 2010 United States Renal Data System (USRDS) Annual Data Report named Taiwan with highest incidence and prevalence of ESRD.

Prevalence peaked in 2008 at 2,311; incidence in 2005 at 435 per million population. Taiwan, America, and Japan had highest ESRD incidences (384, 362, and 288 per million population in 2008, respectively), whereas ESRD incidence was much lower in Europe: e.g., 121 per million in Netherlands. Aging population raises CKD and ESRD prevalence, posing heavy burdens on medical care. Prospective cohort study showed national prevalence as 11.9%, overall CKD awareness only 3.5% in Taiwan.

Elevated albuminuria is a vital early predictor of diabetic nephropathy and progression to CKD or ESRD, plus a factor in cardiovascular disease (CVD) and mortality.

Collaborative meta-analysis proved linkage with all-cause mortality but cardiovascular mortality independently from each other and traditional risk factors in the general population.

Well-established association triggers concern regarding global prevention. To devise optimal preventive strategy, we must know portents. Prior studies identified risk factors in America,

the Netherlands,

and Norway,

with no such angle explored in Taiwan. We rate three-year incidence and baseline predictors for elevated albuminuria in Taiwanese metropolitan adults.

Methods Study design

This population-based, longitudinal Taichung Community Health Study (TCHS) had

three-year follow-up. Details of study population, participants, sampling methods, and

data collection were cited earlier. Here we briefly describe study population and data

collection.

Study population and sampling methods

Target population consisted of Taichung City residents aged 40 and over, evaluated in October 2004. A total of 363,543 area residents during the time of the initiated probe, represented about 4.09% of national population of the same age. Two-stage sampling was used to draw residents, with a sampling probability proportional to sample size within sampling unit of each stage. At each stage, simple random sampling was used. In the first stage, with li (administrative units, equivalent to blocks of households) as sampling unit, 39 lis were randomly selected from 8 city districts. In the second stage, 110 individuals were randomly sampled from each li. A total of 4,280 persons were selected. During household visits, 750 non-eligible ones were excluded from baseline survey due to death (n=18), hospitalization or imprisonment (n=14), living abroad (n=39), moving out of the area (n=411), living in their children’s home (n=7), sampling frame errors (n=59), or not home during three visits by interviewers (n=202). Of 3,530 eligible persons, 2,359 (66.8%) took part and gave complete information. We compared age, gender, and administrative unit of population and sample, noting similar distribution. For baseline survey, we contacted 2,311 survivors. A total of 1,648 (71.3%) original subjects completed follow-up in 2007-2009. Excluding cases with elevated albuminuria at baseline and missing UACR data, current analysis tallied 1,434 (87.0%) with normal albumin excretion at baseline. Study was approved by the Human Research Committee of China Medical University Hospital, with written participants’

consent.

Data collection

-Sociodemographic factors, lifestyle, and history of disease

Age, gender, tobacco, alcohol, betel nut, regular exercise, medical/medication

history, and family history of disease were collected by self-administered

questionnaires. Tobacco, alcohol, betel quid, and family history of cardiovascular- related diseases were divided into two groups. Non-smokers never smoked or had smoked less than 100 cigarettes; smokers currently or previously smoked at least 100 cigarettes during their lifetime. Individuals self-reporting alcohol, betel quid and/or exercise were thus grouped.

-Anthropometric measurements

These were gleaned from complete physical examination by trained staff, weight and height measured by autoanthropometer (super-view, HW-666), with subjects shoeless and wearing light clothing. Body mass index (BMI) was tallied as weight divided by height squared (kg/m

). With a participant standing, waist circumference was measured midway between superior iliac crest and costal margin; hip circumference, calculated at maximum protrusion point of buttocks around the pelvis, waist-to-hip ratio (WHR) as waist in cm divided by hip circumference in cm, to measure of regional fat distribution.

Blood pressure was gauged by electronic device (COLIN, VP-1000, Japan) three times after subjects rested for twenty minutes: i.e., lowest systolic and diastolic recorded.

-Biochemical markers

Blood sample was drawn in the morning after twelve-hour fast and analyzed within four hours, spot morning urine sample collected. Biochemical markers such as total cholesterol, high- (HDL-C) and low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), fasting plasma glucose (FPG), blood urea nitrogen (BUN), uric acid, and creatinine were noted by biochemical autoanalyser (Beckman Coulter, Fullerton, CA) at the Department of Clinical Laboratory (China Medical University Hospital).

Urinary creatinine (Jaffe’s kinetic method) and albumin (colorimetyl bromcresol purple)

were measured by autoanalyser. The interassay and intraassay CVs for fasting plasma

glucose were both 4%; inter- and intraassay CVs for triglyceride 6.8% and 5%,

respectively. HDL-C level was measured by direct HDL-C method, both inter- and intraassay CVs 4.5%. For LDL-C rated by direct LDL-C method, inter- and intraassay CVs 4.5% and 3%, respectively. Interassay precision variation coefficient was <3.0%

for both creatinine and albumin concentrations. Urine albumin-to-creatinine ratio (UACR) (mg/g) was derived from spot morning samples as albumin (mg/dl) divided by creatinine (g/dl). Since 24-hour urine collection was not possible, UACR in the morning urine sample, with which it correlates well, served as a surrogate marker of albumin excretion rate. Elevated albuminuria (micro- or macroalbuminuria) is defined as UACR above 30 mg/g.

Renal function was assessed by estimated GFR (eGFR), using re- expressed Modification of Diet in Renal Disease Study equation: eGFR (ml/min/1.73 m

) = 175 × (serum creatinine (mg/dl))

× (age)

× (0.742 if female),

CKD defined as eGFR < 60 ml/min/1.73 m

.

Normal adult BUN level is defined as 2.5-7.1 mmol/L; normal creatinine for men as 61.9-132.6 µmol/L; for women, 44.2-106.1 µmol/L; normal uric acid as below 416.4 µmol/L (male) or 356.9 µmol/L (female).

-Markers of metabolic syndrome

These (BP, FPG, TG, HDL-C) included (1) high BP (prehypertension: systolic 120- 139 mmHg or diastolic 80-89 mmHg; hypertension: systolic ≥ 140 mmHg, diastolic ≥ 90 mmHg or on antihypertensive drug treatment); (2) elevated FPG (hyperglycemia:

FPG 5.6-6.9 mmol/L; diabetes mellitus: FPG ≥ 7.0 mmol/L or undergoing treatment for elevated glucose); (3) elevated TG (pre-hypertriglyceridemia: TG 1.7-2.3 mmol/L;

hypertriglyceridemia: TG ≥ 2.3 mmol/L or undergoing treatment for elevated triglycerides); and (4) abnormal HDL-C (HDL-C < 1.04 mmol/L in men or < 1.29 mmol/L in women or undergoing treatment for reduced HDL-C).

Statistical analysis

Continuous variables were reported as mean ± standard deviation (abbreviated as SD), categorical variables as number and percentage. With UACR distribution skewed to right, median with interquartile range were presented. Baseline demographic and clinical traits among subjects followed up and those lost to follow-up in TCHS cohort were compared. Two-sample t tests and Wilcoxon rank-sum tests were used for continuous variables, and Chi-square tests were served for categorical variables. Three- year crude and weighted cumulative incidence (95% confidence interval, CI) of elevated albuminuria were tallied by proportionate sampling of age and gender. Crude cumulative incidence of elevated albuminuria with 95% CI was calculated for each risk factor by dividing number of new cases by total persons in that group. Strength of association between risk factors and elevated albuminuria was measured by age- and gender- adjusted odds ratios (ORs) with 95% CI by multivariate logistic regression rated baseline predictors. Backward elimination procedure selected predictors reaching significance of 0.05. Sensitivity analyses excluded patients with diabetes mellitus, antihypertensive medication use, or CKD at baseline, respectively; the same multivariate model-building strategy was used each time. Statistical significance was set at p < 0.05, two-sided test used. All analyses were performed with SAS version 9.2 (SAS Institute Inc, Cary, NC).

Results

Table 1 plots baseline demographic and clinical characteristics of 1,648 participants

in follow-up study and 663 lost to follow-up. Distributions of baseline tobacco, alcohol,

betel nut, BMI, WHR, creatinine, uric acid, hypertension, hypertriglyceridemia, as well

as family history of diabetes mellitus between participants followed up versus those lost

to follow-up, were similar.

Table 2 records three-year crude cumulative incidence of elevated albuminuria, as per various demographic and clinical characteristics at baseline and odds ratios (ORs) of elevated albuminuria in multivariate logistic regression models after controlling for age and gender. Among 1,434 individuals, 67 new elevated albuminuria cases were identified by this follow-up. Three-year crude and age- and gender-weighted cumulative incidence were 4.7% (95% CI: 3.6-5.9) and 4.5% (95% CI: 3.4-5.6), respectively, indicating age and elevated BMI, WHR, BUN, creatinine, uric acid, and UACR at baseline as correlating with cumulative incidence of elevated albuminuria.

Multivariate logistic regression hinted WHR, creatinine, uric acid, and baseline UACR linking with elevated albuminuria (Table 3). Subjects with elevated WHR (OR:

2.2, 95% CI: 1.2-3.9), abnormal creatinine (OR: 3.7, 95% CI: 1.1-12.6), hyperuricemia (OR: 1.8, 95% CI: 1.0-3.3) and elevated baseline UACR (OR: 4.0, 95% CI: 1.1-14.3 for UACR of 3.20-6.39 mg/g; OR: 16.7, 95% CI: 5.0-55.5 for UACR of 6.40-29.99 mg/g) showed higher risk. We also derived a variable to indicate whether an individual had an increase of at least 25% from baseline UACR value, determined by change percentage in UACR. Change percentage in UACR was calculated as followed up UACR minus baseline UACR divided by baseline UACR value and then resulting decimal being converted to percentage. This derived variable was added into multivariate logistic regression analysis to replace baseline UACR. Findings revealed increase of at least 25% from baseline UACR value was significantly correlated with elevated albuminuria (OR: 43.3, 95% CI: 10.4-180.3, P <.001).

To account for confounders of diabetes mellitus, antihypertensive medication use,

and CKD at baseline for elevated albuminuria, we performed sensitivity analyses by

excluding subjects with diabetes mellitus (n=115), antihypertensive medication use

(n=239), and CKD (n=105), respectively. Significant predictors identified were similar

to those without excluding such cases, except for uric acid (Table 4). Findings prove central obesity gauged by WHR, decreased renal function ascertained by creatinine, and baseline UACR as significant predictors associated with elevated albuminuria in the general population.

Discussion

The present study shows three-year incidence of elevated albuminuria as 4.5% in a representative sample of Chinese adults aged 40 years and above in Taichung City, Taiwan. Of all other variables in the multivariate model taken into account, elevated WHR, abnormal creatinine, hyperuricemia, and elevated baseline UACR are pivotal variables that predicted elevated albuminuria during follow-up. Three-year incidence of elevated albuminuria from our study is higher than those in Framingham Offspring Cohort (FOC; America)

and Prevention of REnal and Vascular ENd-stage Disease (PREVEND; Netherlands)

studies. The incidence of elevated albuminuria was 10.0%

in FOC’s 1,916 participants with 9.5 years of follow-up, a little lower in PREVEND’s 5,825 participants with median 9.3-year follow-up. Possible explanation for higher risk of elevated albuminuria in our study is higher mean values of blood pressure but lower mean values of eGFR at baseline than in FOC and PREVEND studies.

We found elevated baseline UACR at normal range had higher risk for elevated

albuminuria in TCHS, consistent with those reported by FOC

and PREVEND

studies. After excluding diabetics, antihypertensive medication use, or CKD at baseline

in our sensitivity analyses, baseline UACR remained a significant predictor. Our data

indicated UACR at normal range is still a significant predictor. For diabetics, the

American Diabetes Association recommends microalbuminuria screening as routine to

monitor renal function and prevent progression of CKD.

Still, evidence in general

population was limited. In these community-based prospective cohort studies, our data

along with those from FOC

and PREVEND

studies point to baseline UACR at normal range as pivotal in elevated albuminuria among the general population.

Previous research showed obesity as measured by WHR

, BMI

, or waist circumstance strongly linked with elevated albuminuria. Chandie et al. cite WHR as an obesity indicator predicting elevated albuminuria incidence,

consistent with our findings. Potential mechanisms to explain association between kidney injury and obesity include inflammatory cytokines (TNF-α, IL-6, CRP) and lipid byproducts that may affect renal function.

Also, lipotoxicity and hemodynamic factors are involved in these mechanisms.

A recent community-based study portends inflammation biomarkers associating with decreased renal function and elevated albuminuria.

Our community-based cohort revealed uric acid predicting elevated albuminuria in a general population aged 40 years and above; prior studies support our results. Elevated uric acid levels reportedly heighten risk in middle-aged and elderly Taiwanese

and among Type 1 diabetes patients in America

and Denmark.

From Japan’s general population aged over 40 years, that study suggested uric acid inducing glomerular damage.

On the contrary, uric acid was not a significant predictor of elevated albuminuria in FOC

and PREVEND

studies. In humans, uric acid emanates from breakdown of purine excreted. Animal study showed uric acid mediating renal disease and its progression

; possible mechanisms include preglomerular arteriopathy, tubulointerstitial inflammation, or epithelial-to-mesenchymal transition of renal tubular cells.

This is the first study prospectively estimating three-year incidence and evaluating

predictors of elevated albuminuria incidence in Chinese adults via random sample. Its

strengths include population-based sample, along with standardized measurements for

exposures and outcomes. There are limitations. First, we collected spot morning urine

specimens, with UACR measured only once in baseline or follow-up. UACR test in a spot urine sample is adopted for convenience, as recommended by some guidelines for measuring elevated albuminuria. Collection of 24-hour urine specimens and timed specimens are not feasible in epidemiologic study of a general population. Previous study revealed higher albuminuria levels in spot urine samples than 24-hour urine samples.

Second, loss to follow-up rate was 28.7%, indicating potential selection bias.

Given that subjects lost to follow-up had slightly higher median or mean baseline UACR and blood pressure but lower eGFR than those with follow-up, overall incidence of elevated albuminuria may be underestimated. Regarding correlation between risk factors and elevated albuminuria, it is unlikely to differ between subjects lost versus those followed up. This potential selection bias was likely non-differential, indicating no bias in estimating odds ratio. Such non-differential selection error might be random and biased results in effect may be toward the null, a lesser threat to validity.

Conclusion

This population-based longitudinal research is the first to rate elevated albuminuria incidence and identify associated factors in a Chinese population with a random sample.

Elevated WHR, abnormal creatinine, hyperuricemia and elevated baseline UACR are risk factors for elevated albuminuria in the general population and may help identify it.

Early screening is critical to early prevention of CKD incident cases.

Competing interests

The authors have no competing interests to declare.

Authors' contributions

Substantial contributions to conception and design; data acquisition, analysis, and

interpretation: all authors; drafting or revising article critical for important intellectual

content: LN Liao and TC Li; final approval of the version published: all authors.

Acknowledgements

This study was supported by grants from the National Science Council of Taiwan

(NSC101-2314-B-039-017-MY3 & NSC 102-2314-B-039-005-MY2), China Medical

University Hospital (DMR-100-131), and Taiwan Ministry of Health and Welfare

Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004).

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Table 1. Baseline demographic and clinical characteristics: participants followed up or lost to follow-up in TCHS cohort (Total N=2,311)

Baseline demographic and clinical characteristics Followed up

(N=1,648) Lost to follow up

(N=663) P-value

Sociodemographic factors

Age (years) 56.1 ± 10.7 57.5 ± 12.5 0.012

40-49 555 (33.7) 241 (36.4) <.001

50-59 562 (34.1) 163 (24.6)

60-69 311 (18.9) 129 (19.5)

≧70 220 (13.4) 130 (19.6)

Male 836 (50.7) 280 (42.2) <.001

Lifestyle behaviors

Smokers 434 (26.4) 194 (29.3) 0.151

Alcohol drinking 484 (29.4) 168 (25.4) 0.053

Betel nut chewing 147 (8.9) 59 (8.9) 0.995

Regular exercise 1133 (68.8) 425 (64.1) 0.028

Anthropometric measurements

BMI (kg/m

) 24.3 ± 3.3 24.4 ± 3.4 0.423

WHR 0.8 ± 0.1 0.8 ± 0.1 0.753

Biochemical markers 　 　

BUN (mmol/L) 4.6 ± 1.5 4.8 ± 2.0 0.010

Creatinine (µmol/L) 78.9 ± 23.6 82.9 ± 49.9 0.052

Uric acid (µmol/L) 335.1 ± 83.9 340.5 ± 84.8 0.158

eGFR (ml/min/1.73 m

) 81.9 ± 21.2 79.1 ± 21.1 0.004

Baseline UACR (mg/g) 5.2 (2.7-11.8) 6.3 (3.4-15.7) <.001

Metabolic syndrome related variables Blood pressure

SBP (mmHg) 134.1 ± 20.9; 138.5 ± 23.5; <.001

DBP (mmHg) 78.5 ± 12.1 79.8 ± 13.0 0.021

Normal 416 (25.2) 151 (22.8) 0.069

Prehypertension

(SBP: 120-139 or DBP: 80-89 mmHg) 509 (30.9) 186 (28.1)

Hypertension 723 (43.9) 326 (49.2)

Triglycerides (mmol/L) 1.4 ± 1.1 1.4 ± 1.0 0.428

Normal 1191 (72.3) 484 (73.0) 0.139

Pre-hypertriglyceridemia (TG: 1.7-2.3 mmol/L) 178 (10.8) 85 (12.8)

Hypertriglyceridemia 279 (16.9) 94 (14.2)

HDL-cholesterol (mmol/L) 1.2 ± 0.3 1.2 ± 0.3 0.976

M: ≧1.04, F: ≧1.29 mmol/L 741 (45.0) 292 (44.0) 0.687

Abnormal 907 (55.0) 371 (56.0)

Fasting plasma glucose (mmol/L) 5.7 ± 1.3 6.0 ± 2.1 0.001

Normal 1032 (62.6) 415 (62.6) <.001

Hyperglycemia (FPG: 5.6-6.9 mmol/L) 441 (26.8) 139 (21.0)

Diabetes mellitus 175 (10.6) 109 (16.4)

Family history of disease

Hypertension 796 (48.3) 270 (40.7) 0.001

Diabetes mellitus 515 (31.3) 182 (27.5) 0.072

Data were presented as mean±SD or median (interquartile ranges) for continuous variables or n (%) for categorical variables. BMI: body mass index; WHR: waist-to-hip ratio; BUN: blood urea nitrogen; eGFR: estimated glomerular filtration rate; UACR:

urine albumin-to-creatinine ratio; SBP: systolic blood press; DBP: diastolic blood press;

TG: triglycerides; FPG: fasting plasma glucose.

a: Two-sample t test for continuous variables or Chi-square test for categorical variables.

b: P-value from Wilcoxon rank-sum test.

Table 2. Three-year cumulative incidence and odds ratios of elevated albuminuria according to various demographic and clinical characteristics at baseline

Baseline demographic and clinical

characteristics At

risk

Developing elevated albuminuria

Cumulative incidence (%, 95% CI)

Odds ratio

(95% CI)

P-value

All 1434 67 4.7 (3.6-5.9) － －

Sociodemographic factors

Age (per 10 years) － － － 1.4 (1.2-1.8)

0.001

40-49 514 18 3.5 (2.1-5.5) 1.0

50-59 498 20 4.0 (2.5-6.1) 1.2 (0.6-2.2)

0.670

60-69 262 14 5.3 (3.0-8.8) 1.6 (0.8-3.2)

0.228

≧70 160 15 9.4 (5.3-15.0) 2.8 (1.4-5.8)

0.005

Gender

Male 708 36 5.1 (3.6-7.0) 1.0

Female 726 31 4.3 (2.9-6.0) 1.0 (0.6-1.6)

0.868

Lifestyle behaviors Smoking status

Non-smokers 1073 47 4.4 (3.2-5.8) 1.0

Smokers 360 19 5.3 (3.2-8.1) 1.3 (0.7-2.5) 0.422

Alcohol drinking

No 1021 48 4.7 (3.5-6.2) 1.0

Yes 412 18 4.4 (2.6-6.8) 1 (0.5-1.8) 0.898

Betel nut chewing

No 1309 60 4.6 (3.5-5.9) 1.0

Yes 123 5 4.1 (1.3-9.2) 1 (0.4-2.5) 0.929

Regular exercise

No 444 22 5.0 (3.1-7.4) 1.0

Yes 988 44 4.5 (3.3-5.9) 0.7 (0.4-1.2) 0.243

Anthropometrics

BMI － － － 1.5 (1.2-1.8)

0.001

< 24 kg/m

724 23 3.2 (2.0-4.7) 1.0

≧24 kg/m

710 44 6.2 (4.5-8.2) 1.9 (1.1-3.3) 0.014

WHR － － － 1.7 (1.3-2.2)

< 0.001

M< 0.9, F< 0.85 1026 33 3.2 (2.2-4.5) 1.0

M≧0.9, F≧0.85 407 34 8.4 (5.9-11.5) 2.6 (1.5-4.3) < 0.001

Biochemical markers

BUN － － － 1.2 (0.9-1.5)

0.196

2.5-7.1 mmol/L 1352 57 4.2 (3.2-5.4) 1.0

Abnormal 82 10 12.2 (6.0-21.3) 2.5 (1.2-5.3) 0.013

Creatinine － － － 1.3 (1.1-1.6)

0.013

M: 61.9-132.6; F: 44.2-106.1 µmol/L 1410 61 4.3 (3.3-5.5) 1.0

Abnormal 24 6 25.0 (9.8-46.7) 5.8 (2.1-15.7) 0.001

Uric acid － － － 1.5 (1.2-1.9)

0.001

M: < 416.4; F: < 356.9 µmol/L 1151 42 3.7 (2.6-4.9) 1.0

Abnormal 283 25 8.8 (5.8-12.8) 2.4 (1.4-4.0) < 0.001

eGFR － － － 0.9 (0.7-1.3)

0.635

≧60 ml/min/1.73m

1329 55 4.1 (3.1-5.4) 1.0 　

<60 ml/min/1.73m

105 12 11.4 (6.1-19.1) 2.1 (1.0-4.4) 0.037

Baseline UACR － － － 2.2 (1.8-2.6)

< 0.001

1

(≦3.19 mg/g) 474 3 0.6 (0.1-1.8) 1.0

2

(3.20-6.39 mg/g) 474 14 3.0 (1.6-4.9) 4.7 (1.3-16.4) 0.016 3

(6.40-29.99 mg/g) 486 50 10.3 (7.7-13.3) 17.3 (5.3-56.4) < 0.001 Metabolic syndrome related variables

Blood pressure

SBP (mmHg) － － － 1.3 (1-1.7)

; 0.020

DBP (mmHg) － － － 1.5 (1.2-1.9)

0.001

Normal 396 7 1.8 (0.7-3.6) 1.0

Prehypertension

(SBP: 120-139 or DBP: 80-89 mmHg) 470 17 3.6 (2.1-5.7) 1.9 (0.8-4.7) 0.156

Hypertension 568 43 7.6 (5.5-10.1) 3.7 (1.6-8.8) 0.003

Triglycerides － － － 1.2 (1-1.4)

0.104

Normal 1063 40 3.8 (2.7-5.1) 1.0

Pre-hypertriglyceridemia

(TG: 1.7-2.3 mmol/L) 148 12 8.1 (4.3-13.7) 2.4 (1.2-4.6) 0.013

Hypertriglyceridemia 223 15 6.7 (3.8-10.9) 1.7 (0.9-3.2) 0.085

HDL-cholesterol － － － 0.7 (0.5-0.9)

0.009

M: ≧1.04, F: ≧1.29 mmol/L 665 22 3.3 (2.1-5.0) 1.0 　

Abnormal 769 45 5.9 (4.3-7.8) 1.7 (1.0-2.9) 0.038

Fasting plasma glucose － － － 1.3 (1.1-1.5)

0.003

Normal 937 34 3.6 (2.5-5.0) 1.0

Hyperglycemia

(FPG: 5.6-6.9 mmol/L) 382 20 5.2 (3.2-8.0) 1.3 (0.7-2.4) 0.338

Diabetes mellitus 115 13 11.3 (6.2-18.6) 2.7 (1.3-5.4) 0.006

Family history of disease Hypertension

No 734 36 4.9 (3.5-6.7) 1.0

Yes 700 31 4.4 (3.0-6.2) 1.1 (0.7-1.9) 0.682

Diabetes mellitus

No 992 49 4.9 (3.7-6.5) 1.0

Yes 442 18 4.1 (2.4-6.4) 1.0 (0.5-1.7) 0.892

BMI: body mass index; WHR: waist-to-hip ratio; BUN: blood urea nitrogen; eGFR:

estimated glomerular filtration rate; UACR: urine albumin-to-creatinine ratio; SBP:

systolic blood press; DBP: diastolic blood press; TG: triglycerides; FPG: fasting plasma

glucose.

a: Odds ratio and p-value calculated by logistic regression, adjusting for age and gender.

b: Only gender adjusted.

c: Only age adjusted.

†: Using continuous variables and per 1 SD increase.

Table 3. Independent variables associated with elevated albuminuria in TCHS cohort population after three-year follow-up

Independent variables (at baseline) Odds ratio

(95% CI)

P-value

WHR

M: ≧0.9, F: ≧0.85 (vs. M: < 0.9, F:< 0.85) 2.2 (1.2-3.9) 0.009 Creatinine

Abnormal (vs. M: 61.9-132.6; F: 44.2-106.1 µmol/L) 3.7 (1.1-12.6) 0.037 Uric acid

Abnormal (vs. M: < 416.4; F: < 356.9 µmol/L) 1.8 (1.0-3.3) 0.047 Baseline UACR

2

(3.20-6.39) (vs. 1

(≦3.19 mg/g)) 4.0 (1.1-14.3) 0.033 3

(6.40-29.99) (vs. 1

(≦3.19 mg/g)) 16.7 (5.0-55.5) <.001 WHR: waist-to-hip ratio; UACR: urine albumin-to-creatinine ratio.

a: Odds ratio and p-value calculated by multivariate logistic regression, adjusting for

age, gender, lifestyle (tobacco, alcohol, betel nut, regular exercise), antihypertensive

medication use, hypertension, hypertriglyceridemia, abnormal HDL-C, and diabetes

mellitus.

Table 4. Sensitivity analysis: independent variables associated with elevated albuminuria in TCHS cohort population after three-year follow-up

Independent variables Odds ratio

(95% CI) P-value No diabetes mellitus at bseline

(n=1,319; elevated albuminuria cases=54)

WHR

M: ≧0.9, F: ≧0.85 (vs. M: < 0.9, F:< 0.85) 2.6 (1.3-5.0) 0.005 Creatinine

Abnormal (vs. M: 61.9-132.6; F: 44.2-106.1 µmol/L) 7.1 (1.5-34.4) 0.015 Uric acid

Abnormal (vs. M: < 416.4; F: < 356.9 µmol/L) 1.5 (0.7-3.0) 0.251 Baseline UACR

2

(3.20-6.39) (vs. 1

(≦3.19 mg/g)) 5.1 (1.1-23.4) 0.037 3

(6.40-29.99) (vs. 1

(≦3.19 mg/g)) 25.0 (5.8-107.3) <.001 No antihypertensive medication use at bseline

(n=1,195; elevated albuminuria cases=43)

WHR

M: ≧0.9, F: ≧0.85 (vs. M: < 0.9, F:< 0.85) 2.1 (1.0-4.4) 0.057 Creatinine

Abnormal (vs. M: 61.9-132.6; F: 44.2-106.1 µmol/L) 6.1 (1.1-33.1) 0.037 Uric acid

Abnormal (vs. M: < 416.4; F: < 356.9 µmol/L) 2.0 (0.9-4.2) 0.087 Baseline UACR

2

(3.20-6.39) (vs. 1

(≦3.19 mg/g)) 2.6 (0.7-9.9) 0.168 3

(6.40-29.99) (vs. 1

(≦3.19 mg/g)) 13.1 (3.8-44.6) <.001 No CKD at bseline

(n=1,329; elevated albuminuria cases=55)

WHR

M: ≧0.9, F: ≧0.85 (vs. M: < 0.9, F:< 0.85) 2.2 (1.2-4.2) 0.016 Creatinine

Abnormal (vs. M: 61.9-132.6; F: 44.2-106.1 µmol/L) 6.6 (0.6-73.7) 0.126 Uric acid

Abnormal (vs. M: < 416.4; F: < 356.9 µmol/L) 1.6 (0.8-3.2) 0.145 Baseline UACR

2

(3.20-6.39) (vs. 1

(≦3.19 mg/g)) 3.0 (0.8-11.2) 0.095 　 3

(6.40-29.99) (vs. 1

(≦3.19 mg/g)) 13.7 (4.1-45.9) <.001

WHR: waist-to-hip ratio; UACR: urine albumin-to-creatinine ratio.

a: Odds ratio and p-value derived by multivariate logistic regression, adjusting for age, gender, lifestyle (tobacco, alcohol, betel nut, regular exercise), antihypertensive medication use, hypertension, hypertriglyceridemia, and abnormal HDL-C.

b: Odds ratio and p-value derived by multivariate logistic regression, adjusting for age,

gender, lifestyle (tobacco, alcohol, betel nut, regular exercise), hypertension, hypertriglyceridemia, abnormal HDL-C, and diabetes mellitus.

c: Odds ratio and p-value derived by multivariate logistic regression after adjustment for

age, gender, lifestyle (tobacco, alcohol, betel nut, regular exercise), antihypertensive

medication use, hypertension, hypertriglyceridemia, abnormal HDL-C, and diabetes

mellitus.